针对传统固定权重多目标无功优化在应对新型电力系统复杂多变的工况时无法针对实时工况做出最合适的控制决策的问题,提出一种自适应多目标无功优化控制策略。该策略以系统有功网损和并网点电压偏离量的加权最小作为目标函数,目标函数的...针对传统固定权重多目标无功优化在应对新型电力系统复杂多变的工况时无法针对实时工况做出最合适的控制决策的问题,提出一种自适应多目标无功优化控制策略。该策略以系统有功网损和并网点电压偏离量的加权最小作为目标函数,目标函数的权重系数根据并网点电压的偏离情况自适应调节。首先,分析海上风电场并网点电压波动与有功、无功输出的关系,建立相应的无功分配模型,并针对风电机组及静止无功发生器(static var ge nerator,SVG)的输入输出特性,建立相应的无功控制模型。此外,考虑海上运行的功率约束、安全运行约束等,采用变惯性权重粒子群优化算法对无功控制策略进行求解。最后,在MATLAB中搭建海上风电场模型进行仿真验证,仿真算例表明:相较于传统固定权重多目标无功优化,自适应多目标无功优化控制策略可以根据电网实时工况,迅速调整各优化目标的优先级,较好地实现有功网损和并网点电压的协调优化。展开更多
The paper presents a procedure to design water network. First of all, water reuse system, water regeneration reuse system (including regeneration recycle) and wastewater treatment system are designed separately. But t...The paper presents a procedure to design water network. First of all, water reuse system, water regeneration reuse system (including regeneration recycle) and wastewater treatment system are designed separately. But the interaction between different parts demands that each part is designed iteratively to optimize the whole water network. Therefore, on the basis of the separated design a water netvrork superstructure including reuse, regeneration and wastewater treatment is established from the system engineering point of view. And a multi-objective adaptive simulated annealing genetic algorithm is adopted to simultaneously integrate the overall water netvrork to balance the economic and environmental effects. The algorithm overcomes the defect of local optimum of simulated annealing (SA), avoids the pre-maturation of genetic algorithm (GA) and finds a set of solutions (pareto front) in acceptable computer time. Prom the pareto front, a point with minimum fresh water consumption will be extended to zero discharge as our ultimate goal.展开更多
In recent years, sinmlated annealing algo-rithms have been extensively developed and uti-lized to solve nmlti-objective optimization problems. In order to obtain better optimization perfonmnce, this paper proposes a N...In recent years, sinmlated annealing algo-rithms have been extensively developed and uti-lized to solve nmlti-objective optimization problems. In order to obtain better optimization perfonmnce, this paper proposes a Novel Adaptive Simulated Annealing （NASA） algorithm for constrained multi-objective optimization based on Archived Multi-objective Simulated Annealing （AMOSA）. For han-dling multi-objective, NASA makes improverrents in three aspects： sub-iteration search, sub-archive and adaptive search, which effectively strengthen the stability and efficiency of the algorithnm For handling constraints, NASA introduces corresponding solution acceptance criterion. Furtherrrore, NASA has also been applied to optimize TD-LTE network perform-ance by adjusting antenna paranleters; it can achieve better extension and convergence than AMOSA, NS-GAII and MOPSO. Analytical studies and simulations indicate that the proposed NASA algorithm can play an important role in improving multi-objective optimi-zation performance.展开更多
Considering comprehensive benefit of micro-grid system and consumers,we establish a mathematical model with the goal of the maximum consumer satisfaction and the maximum benefit of power generation side in the view of...Considering comprehensive benefit of micro-grid system and consumers,we establish a mathematical model with the goal of the maximum consumer satisfaction and the maximum benefit of power generation side in the view of energy management.An improved multi-objective local mutation adaptive quantum particle swarm optimization(MO-LM-AQPSO)algorithm is adopted to obtain the Pareto frontier of consumer satisfaction and the benefit of power generation side.The optimal solution of the non-dominant solution is selected with introducing the power shortage and power loss to maximize the benefit of power generation side,and its reasonableness is verified by numerical simulation.Then,translational load and time-of-use electricity price incentive mechanism are considered and reasonable peak-valley price ratio is adopted to guide users to actively participate in demand response.The simulation results show that the reasonable incentive mechanism increases the benefit of power generation side and improves the consumer satisfaction.Also the mechanism maximizes the utilization of renewable energy and effectively reduces the operation cost of the battery.展开更多
文摘针对传统固定权重多目标无功优化在应对新型电力系统复杂多变的工况时无法针对实时工况做出最合适的控制决策的问题,提出一种自适应多目标无功优化控制策略。该策略以系统有功网损和并网点电压偏离量的加权最小作为目标函数,目标函数的权重系数根据并网点电压的偏离情况自适应调节。首先,分析海上风电场并网点电压波动与有功、无功输出的关系,建立相应的无功分配模型,并针对风电机组及静止无功发生器(static var ge nerator,SVG)的输入输出特性,建立相应的无功控制模型。此外,考虑海上运行的功率约束、安全运行约束等,采用变惯性权重粒子群优化算法对无功控制策略进行求解。最后,在MATLAB中搭建海上风电场模型进行仿真验证,仿真算例表明:相较于传统固定权重多目标无功优化,自适应多目标无功优化控制策略可以根据电网实时工况,迅速调整各优化目标的优先级,较好地实现有功网损和并网点电压的协调优化。
文摘The paper presents a procedure to design water network. First of all, water reuse system, water regeneration reuse system (including regeneration recycle) and wastewater treatment system are designed separately. But the interaction between different parts demands that each part is designed iteratively to optimize the whole water network. Therefore, on the basis of the separated design a water netvrork superstructure including reuse, regeneration and wastewater treatment is established from the system engineering point of view. And a multi-objective adaptive simulated annealing genetic algorithm is adopted to simultaneously integrate the overall water netvrork to balance the economic and environmental effects. The algorithm overcomes the defect of local optimum of simulated annealing (SA), avoids the pre-maturation of genetic algorithm (GA) and finds a set of solutions (pareto front) in acceptable computer time. Prom the pareto front, a point with minimum fresh water consumption will be extended to zero discharge as our ultimate goal.
基金supported by the Major National Science & Technology Specific Project of China under Grants No.2010ZX03002-007-02,No.2009ZX03002-002,No.2010ZX03002-002-03
文摘In recent years, sinmlated annealing algo-rithms have been extensively developed and uti-lized to solve nmlti-objective optimization problems. In order to obtain better optimization perfonmnce, this paper proposes a Novel Adaptive Simulated Annealing （NASA） algorithm for constrained multi-objective optimization based on Archived Multi-objective Simulated Annealing （AMOSA）. For han-dling multi-objective, NASA makes improverrents in three aspects： sub-iteration search, sub-archive and adaptive search, which effectively strengthen the stability and efficiency of the algorithnm For handling constraints, NASA introduces corresponding solution acceptance criterion. Furtherrrore, NASA has also been applied to optimize TD-LTE network perform-ance by adjusting antenna paranleters; it can achieve better extension and convergence than AMOSA, NS-GAII and MOPSO. Analytical studies and simulations indicate that the proposed NASA algorithm can play an important role in improving multi-objective optimi-zation performance.
基金National Natural Science Foundation of China(No.519667013)Institution of Higher Learning Scientific Research Project of Gansu Province of China(No.2016B-032)。
文摘Considering comprehensive benefit of micro-grid system and consumers,we establish a mathematical model with the goal of the maximum consumer satisfaction and the maximum benefit of power generation side in the view of energy management.An improved multi-objective local mutation adaptive quantum particle swarm optimization(MO-LM-AQPSO)algorithm is adopted to obtain the Pareto frontier of consumer satisfaction and the benefit of power generation side.The optimal solution of the non-dominant solution is selected with introducing the power shortage and power loss to maximize the benefit of power generation side,and its reasonableness is verified by numerical simulation.Then,translational load and time-of-use electricity price incentive mechanism are considered and reasonable peak-valley price ratio is adopted to guide users to actively participate in demand response.The simulation results show that the reasonable incentive mechanism increases the benefit of power generation side and improves the consumer satisfaction.Also the mechanism maximizes the utilization of renewable energy and effectively reduces the operation cost of the battery.
